The invention relates to a method and an arrangement for transforming an input variable.
Transforming variable input signals is a task that must be performed in numerous areas of digital signal processing. Often, the objective is to extract the problem-relevant content of an input signal encompassing a large variation range, with as little information as possible being lost.
For example, in machine-based image evaluation, a typical problem associated with an input signal present in the form of binary pixel values of a raster image is determining whether the investigated image or a portion thereof can be categorized under one of possibly several specific, predetermined object classes. The major-axis transformation and the so-called neural networks, for example, are frequently used in this connection. Because of the necessary computing capacity, however, these known procedures demonstrate a limited adaptability to arbitrary images, and only a low data throughput in real-time operation.
It is the object of the present invention to provide a method and an arrangement for transforming an input variable with a high adaptability to a priori, arbitrary images and a high processing speed.
Solutions to this object in accordance with the invention are disclosed in the independent claims. The dependent claims list advantageous embodiments and modifications of the invention.
In principle, the invention permits the realization of arbitrary, discrete transformations without arithmetic operations. The dimension of the partial-assignment specifications can advantageously be kept small. The transformation can be effected with the use of a programmable digital computer, as well as by means of a network having electronic modules in the network nodes and connecting lines between the nodes. The use of a network is especially advantageous for the real-time processing of digital signals, for example in image recognition, because of the attainable high processing speed. The components used as network nodes are preferably programmable read-only memories, particularly so-called EEPROMS.